Controlled Trial: Impact of AI on Dentists’ Gaze in Caries Detection

GERMANY: A recent study conducted by researchers from the Department of Oral Diagnostics, Digital Health, and Health Services Research at Charité – Universitätsmedizin Berlin delves into the profound impact of artificial intelligence (AI) on dentists’ gaze during caries detection. 

The paper, titled “Impact of artificial intelligence on dentists’ gaze during caries detection: A randomized controlled trial,” authored by Lubaina T. Arsiwala-Scheppach, Nora J. Castner, Csaba Rohrer, Sarah Mertens, Enkelejda Kasneci, Jose Eduardo Cejudo Grano de Oro, and Falk Schwendicke, was published in the Journal of Dentistry.

Objective: Understanding AI’s Influence on Dentists’ Gaze

The primary objective of the study was to comprehensively understand how the integration of artificial intelligence influences dentists’ gaze behaviour during the detection of primary proximal carious lesions on bitewing radiographs.

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The research involved 22 dentists who assessed a median of 18 bitewing images, resulting in 170 datasets from dentists without AI and 179 datasets from dentists using AI. The study analyzed various parameters, including time to first fixation, fixation count, average fixation duration, fixation frequency, and transitional patterns of dentists’ gaze.

Key Findings: AI Enhances Efficiency in Gaze Patterns

The results showcased that dentists using AI exhibited more efficient viewing behavior compared to those without AI. Dentists assisted by AI demonstrated a shorter time to first fixation and more fixations on teeth with restorations. Notably, the visual search strategy employed by dentists with AI was found to be less systematic, indicating a different approach to image analysis.

This study’s clinical significance lies in unraveling the impact of AI-generated annotations on dentists’ gaze patterns during caries detection. Understanding these nuances can contribute to the refinement and customization of AI-based diagnostic tools, potentially reducing extraneous attentional processing for dentists and allowing for a more thorough examination of dental images.

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Future Implications: Safeguarding Against Diagnostic Biases

The study also sheds light on the need for a deeper understanding of how AI influences the diagnostic process, directing attention to relevant areas of interest and potentially reducing attention to irrelevant features. This knowledge is crucial for improving AI systems, addressing diagnostic biases during dental education, and ensuring the effective integration of AI into clinical practices.

In conclusion, this randomized controlled trial offers valuable insights into the interaction between dentists and AI, paving the way for advancements in diagnostic support tools and fostering a more efficient and accurate approach to caries detection in dental practice.